A Comparison of Cluster Analyses and Clustering of Sensory Data on Hanwoo BullsKim, Jae-Hee; Ko, Yoon-Sil;

Abstract

Cluster analysis is the automated search for groups of related observations in a data set. To group the observations into clusters many techniques has been proposed, and a variety measures aimed at validating the results of a cluster analysis have been suggested. In this paper, we compare complete linkage, Ward`s method, K-means and model-based clustering and compute validity measures such as connectivity, Dunn Index and silhouette with simulated data from multivariate distributions. We also select a clustering algorithm and determine the number of clusters of Korean consumers based on Korean consumers` palatability scores for Hanwoo bull in BBQ cooking method.